An immune related signature inhibits the occurrence and development of serous ovarian cancer by affecting the abundance of dendritic cells

Abstract

Serous ovarian cancer is one of the major causes of cancer related death among women worldwide. The advanced diagnosis worsens the prognosis of patients with serous ovarian cancer. The immune system has an important impact on the progression of ovarian cancer. Herein, we aimed to establish an immune related prognostic signature to assist in the early diagnosis, treatment, and prognostic evaluation of patients with serous ovarian cancer. Multiple public data sets and immune related genes were obtained from various online public databases, and immune related prognostic signatures were developed through differential expression analysis, univariate Cox proportional hazard regression analysis, and the Least Absolute Shrinkage and Selection Operator (LASSO) Cox regression model. The nomogram model, Kaplan–Meier survival curve analysis, receiver operating characteristic (ROC) curve analysis, and decision curve analysis showed that this signature had a good prediction potential. In conclusion, an immune related signature with good prediction efficiency was established through systematic bioinformatics analysis, which may play a tumor inhibition role by affecting the abundance of activated dendritic cells.